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Effect of extreme temperatures on daily emergency room visits for mental disorders

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Abstract

Relatively few studies investigated the effects of extreme temperatures (both heat and cold) on mental health (ICD-9: 290-319; ICD-10: F00-F99) and the potential effect modifications by individuals’ age, sex, and race. We aimed to explore the effect of extreme temperatures of both heat and cold on the emergency room (ER) visits for mental health disorders, and conducted a stratified analysis to identify possible susceptible population in Erie and Niagara counties, NY, USA. To assess the short-term impacts of daily maximum temperature on ER visits related to mental disorders (2009–2015), we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, long-term time trend, and seasonality. We found that there were positive associations between short-term exposure to extreme ambient temperatures and increased ER visits for mental disorders, and the effects can vary by individual factors. We found heat effect (relative risk (RR) = 1.16; 95% confidence intervals (CI), 1.06–1.27) on exacerbated mental disorders became intense in the study region and subgroup of population (the elderly) being more susceptible to extreme heat than any other age group. For extreme cold, we found that there is a substantial delay effect of 14 days (RR = 1.25; 95% CI = 1.08–1.45), which is particularly burdensome to the age group of 50–64 years old and African-Americans. Our findings suggest that there is a positive association between short-term exposure to extreme ambient temperature (heat and cold) and increased ER visits for mental disorders, and the effects vary as a function of individual factors, such as age and race.

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Data availability

The datasets (emergency room visits) analyzed during the current study are not publicly available due to the privacy of patients.

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Authors and Affiliations

  1. Department of Geography, State University of New York at Buffalo, Buffalo, NY, USA

    Eun-hye Yoo & Youngseob Eum

  2. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China

    Qi Gao

  3. Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA

    Qi Gao & Kai Chen

  4. Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA

    Qi Gao & Kai Chen

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  1. Eun-hye Yoo
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  2. Youngseob Eum
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  4. Kai Chen
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Correspondence to Eun-hye Yoo.

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Responsible Editor: Lotfi Aleya

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Author contribution

Eun- Hye Yoo: conceptualization, data curation, formal analysis, methodology, writing–original draft. Youngseob Eum: data curation, writing–original draft. Qi Gao: writing–original draft. Kai Chen: conceptualization, methodology, writing–original draft

Appendices

Appendix 1: Climate data

There are a total of 81 weather stations in Erie/Niagara counties and their locations are presented in Fig. 4. All stations reported daily precipitation data, whereas only 7 stations reported daily temperature.

Fig. 4
figure 4

Weather stations in Erie/Niagara counties

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Appendix 2: Sensitivity analysis

We performed sensitivity analyses to determine optimal parameters for the maximum lag in the distributed lag nonlinear model (DLNM). For the purpose of objective comparison and assessment, we used generalized cross-validation criterion (GCV) and the Quasi-Akaike information criterion (QAIC), in addition to the visual inspection of lagged effects. For QAIC, we standardized the QAIC values obtained at each lag period based on the maximum lag values so that the results are robust to the choice of the maximum lag period considered. The top panel of Fig. 5 illustrates the GCV scores plotted versus different lag periods. The changes in GCV scores indicate that the influence of lag period on the DLNM model fits, where the GCV scores decrease as the lag period increases forming a cyclical pattern. Similarly, QAIC scores in the bottom panel also show that the best fit is achieved at a longer lag period than short periods.

Fig. 5
figure 5

QAIC and GCV, respectively

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We also examined the sensitivity of the cumulative relative risk (RR) associated with ER visits for mental disorders by varying modeling choices. Specifically, we computed RR attributable to extreme ambient temperature (heat and cold) with respect to different values for the degree of freedom for temperature, lag-response, seasonality, and model specification with/without predictors, including length of day and the three air pollutants of NO2, PM2.5, and SO2. As summarized in Table 3, the results indicated that the model fit is robust to the choice of degree of freedom for variables. Similarly, both the length of day and three pollutants have little effects on the cumulative RR.

Table 3 Sensitivity Analysis at maximum lag day of 14 days
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Appendix: 3: Effect modification by air pollution

We examined effect modification of extreme temperature by air pollutants by dividing each air pollutant of NO2, PM2.5, and SO2 into two levels: high (> median value) and low (≤ median value). We introduced the interaction term between categorized air pollutant and a penalized distributed lag nonlinear temperature term for each pollutant. The penalized distributed lag nonlinear temperature term was characterized as a cross-basis matrix. We also included the air pollutant in the model as a linear continuous term in the model for potential residual confounding. The overall cumulative exposure-response curves for temperature and ER visits for mental disorders were estimated along the temperature distribution with a minimum ER visit temperature (MERT) between the 2.5th and the 97.5th percentiles as the reference temperature (Chen et al. 2018b).

Figure 6 presented the estimates of the exposure-response relationship between extreme ambient temperature and the ER visit for mental disorders at low and high air pollution levels for NO2, PM2.5, and SO2, respectively. We also tested the statistical significance of the exposure-response relationship between extreme ambient temperature and the ER visit for mental disorder at low and high air pollution levels using a multivariate Wald test based on the reduced coefficients of the cross-basis matrix of temperature (Gasparrini et al. 2015a). The results of the multivariate Wald test indicated no evidence (p < 0.05) of significance differences in the exposure-response curves for the ER visit for mental disorder stratified by NO2, PM2.5, and SO2 levels. In addition, we assessed the non-linearity of the air pollution effect on ER visits. Specifically, we added a natural cubic spline for each pollutant with a 3 df in the main formula in Eq. 1 using a generalized additive model. The result indicated that all three air pollutants (NO2, PM2.5, and SO2) were not significant with p values of 0.178, 0.863, and 0.121, respectively. The estimates of the non-linear effect of three air pollutants on ER visits for mental disorders are presented in Fig. 7.

Fig. 6
figure 6

Modified overall cumulative temperature-ER visit by mental disorders by air pollution (NO2, PM2.5, and SO2) with 95% CIs. Blue lines represent a low air pollution level and red lines represent a high air pollution level. Dotted lines represent the minimum mortality temperature, and the dashed lines represent the 2.5 and the 97.5th percentiles of ambient daily temperature distribution. The y-axis represents the relative risk of temperature on daily ER visits for mental disorders to the minimum ER visit temperature (MERT). p value is the result of significant test between air pollution levels, based on a multivariate Wald test

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Fig. 7
figure 7

Concentration-response curve for the association between air pollution (NO2, PM2.5, and SO2) and ER visit by mental disorders. The y-axis represents the relative risk of air pollutant concentration on daily ER visits for mental disorders

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Yoo, Eh., Eum, Y., Gao, Q. et al. Effect of extreme temperatures on daily emergency room visits for mental disorders. Environ Sci Pollut Res 28, 39243–39256 (2021). https://doi.org/10.1007/s11356-021-12887-w

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